Technical Team Lead / Pod Lead for Data Engineering and Analytics

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Job Title: Remote Team Lead (100% Work From Home)

Location: Remote from Latin America

Position Type: Full-time

Salary: $3,500 USD/month

Schedule: 40 hours per week. This role offers flexible scheduling autonomy (make your own hours), provided there is sufficient overlap to attend required meetings and effectively collaborate with team members and US-based stakeholders.

Job Overview:

Our Client is a premier analytics consulting firm specializing in data engineering, advanced business intelligence (BI), automation, and enterprise AI-related infrastructure. Operating at the cutting edge of professional tech services, they partner with diverse corporate clients to turn messy data landscapes into highly reliable, revenue-generating technical assets. They move fast, champion strict engineering standards, and value high-trust leaders who view technical delivery as true business ownership.

The Role

Our Client is seeking a highly operational Technical Team Lead / Pod Lead to own the technical execution, quality, and delivery economics of an engineering pod. This is not a passive, hands-off management role, nor is it merely a senior individual contributor position. The Team Lead is fully accountable for translating client statements of work (SOWs) into pristine technical blueprints, conducting rigorous peer code reviews, managing delivery risks, and mentoring a small team of data and BI engineers. Partnering closely with Project and Consulting Managers, this role ensures every deliverable is polished, accurate, and client-ready before release.

Key Responsibilities

Technical Delivery & Architecture Ownership

• Translates complex client project requirements and SOW deliverables into granular, actionable engineering tasks.

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• Defines the foundational technical approach, data schema architectures, and pipeline structures for each phase of delivery.
• Reviews all engineering code, data models, and BI configurations before they reach client environments to eliminate preventable errors.
• Maintains production-ready repositories, robust data pipelines, standard documentation, and deployment configurations.

Quality Assurance, Standards & Execution

• Enforces strict engineering, version-control, and code documentation standards within the pod.
• Designs and implements repeatable QA validation processes to ensure all client-facing artifacts are polished and stable.
• Leads engineering standups and technical alignment conversations, monitoring team progress, velocity, and roadblocks.

Capacity, Economics & Pod Utilization

• Holds responsibility for the pod’s operational efficiency, ensuring engineers remain productively utilized on approved, revenue-generating client work.
• Identifies upcoming resource capacity gaps early and communicates delivery risks to Project Managers.
• Builds reusable code frameworks, templates, and delivery accelerators to automate repetitive tasks and maximize revenue output per engineer.
• Prevents avoidable rework, write-offs, and scope creep through rigorous technical oversight.

Mentorship & Risk Management

• Coaches engineers on data modeling, cloud architecture, query optimization, and technical delivery best practices.
• Pinpoints technical, scope, and third-party dependencies early, proactively crafting mitigation plans.
• Contributes to shared firm-wide libraries and supports cross-project architectural consistency.

Qualifications, Skills, and Key Competencies

Required / Non-Negotiable:

• Experience: 5+ years of professional experience in Data Engineering, Analytics Engineering, Business Intelligence, or high-level Technical Consulting.
• Leadership Stature: Proven experience leading technical delivery pipelines or managing small engineering teams across multiple parallel workstreams.
• Database & Pipeline Mastery: Expert-level SQL proficiency and advanced data modeling design principles.
• Modern Data Stack: Working familiarity with modern data stack architectures, data pipelines, semantic modeling, and code review methodologies.
• Communication: Exceptional written and verbal English communication skills; highly comfortable translating complex technical realities into clear business updates for stakeholders.
• Mindset: A high-ownership operator who thrives under clear accountability for technical and economic outcomes.

Nice-to-have:

• Prior experience within a technical consulting firm, professional services agency, or digital engineering vendor environment.
• Deep hands-on experience across tools like Domo, Snowflake, Power BI/Fabric, Azure, Looker, or dbt.
• Experience managing resource utilization metrics, engineering capacity planning, or professional billability parameters.
• Exposure to building custom AI, LLM integrations, data orchestration tools, or automated data product frameworks.

Benefits:

• Elite Technical Environment: Own the engineering direction for high-impact data products and enterprise client architectures.
• Schedule Flexibility: High autonomy to control and structure your own 40-hour weekly roadmap.
• True Architectural Autonomy: Move freely between high-level system design, hands-on data review, and team coaching.
• Professional Scale: Gain experience managing the economic performance and throughput of a consulting business unit.
• Remote Infrastructure: 100% remote contractor positioning within a collaborative, scalable US data consulting ecosystem.